Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fdbc38f2f98>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fdbc3838080>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    # Define input paramter
    real_inputs = tf.placeholder(tf.float32,
                                 (None, image_width, image_height, image_channels), 
                                 name='real_inputs')
    
    z_input = tf.placeholder(tf.float32,
                             (None, z_dim),
                             name='z_inputs')
    
    lr = tf.placeholder(tf.float32,
                        name='learning_rate')
    
    return real_inputs, z_input, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    # Note that input layer is 28 x 28 x image_channel
    
    # Define leaky relu_function (alpha = 0.2)
    alpha = 0.2
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    
    # Define conv_layer function
    def conv_layer(inputs, filters, kernel_size, strides, batch_norm=True):
        hidden = tf.layers.conv2d(inputs,
                                  filters,
                                  kernel_size,
                                  strides,
                                  padding='same',
                                  kernel_initializer=tf.contrib.layers.xavier_initializer())
        if batch_norm:
            hidden = tf.layers.batch_normalization(hidden, training=True)
        
        return tf.layers.dropout(leaky_relu(hidden), 0.3)
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # First layer
        h0 = conv_layer(images, 64, 5, 2, batch_norm=False) # 14 x 14
        h1 = conv_layer(h0, 128, 5, 2) # 7 x 7
        h2 = conv_layer(h1, 256, 3, 2) # 4 x 4
        h2 = conv_layer(h1, 512, 3, 1) # 4 x 4
        
        # Logits and outputs
        flat = tf.reshape(h2, (-1, 4 * 4 * 512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    # Define leaky relu_function (alpha = 0.2)
    alpha = 0.2
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    
    # Define initialiser
    k_init = tf.contrib.layers.xavier_initializer()
    
    # def deconv
    def deconv(inputs, filters, kernel_size, strides, training, init = k_init):
        
        dec_layer = tf.layers.conv2d_transpose(inputs,
                                               filters,
                                               kernel_size, 
                                               strides, 
                                               padding='same', 
                                               kernel_initializer=k_init)

        dec_layer = tf.layers.batch_normalization(dec_layer, training=training)
        
        # Apply dropout
        return tf.layers.dropout(leaky_relu(dec_layer), 0.5, training=training)
        
    with tf.variable_scope('generator', reuse=not is_train):
        
        
        h1 = tf.layers.dense(z, 7 * 7 * 512)
        h1 = tf.reshape(h1, (-1, 7, 7, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = leaky_relu(h1) # 7 x 7
        
        h2 = deconv(h1, 256, 3, 2, training=is_train) # 14 x 14
        h3 = deconv(h2, 128, 5, 2, training=is_train) # 28 x 28
        h4 = deconv(h3, 64, 5, 1, training=is_train)
        
        logits = tf.layers.conv2d_transpose(h4, out_channel_dim, 5, 1, 'same') # 28 x 28
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    # Ouputs of generator and discriminator
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # Smoothing factor
    smooth = 0.1
    
    # Calculate losses
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                labels=tf.ones_like(d_model_real) * (1-smooth)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get variables
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list = d_vars)
        g_train = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
    
        return d_train, g_train


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    # Build input placeholders
    _, image_width, image_height, image_channels = data_shape
    
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    
    # Define losses
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    
    # Definie optimisers
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for e in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps += 1
                
                # MNIST dataset is between -0.5 and 0.5. Need to fix it by multiplying by 2
                batch_images = batch_images * 2.0
           
                # Sample random data from the generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimisers
                _ = sess.run(d_opt, feed_dict={input_real : batch_images, 
                                               input_z : batch_z, 
                                               lr : learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real : batch_images,
                                               input_z : batch_z, 
                                               lr : learning_rate})
                
                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(e+1, epoch_count),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [23]:
batch_size = 64
z_dim = 100
learning_rate = 1e-3
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 1.4064... Generator Loss: 1.0503
Epoch 1/2... Batch 20... Discriminator Loss: 1.1966... Generator Loss: 1.1706
Epoch 1/2... Batch 30... Discriminator Loss: 1.0354... Generator Loss: 1.2221
Epoch 1/2... Batch 40... Discriminator Loss: 1.0484... Generator Loss: 2.6650
Epoch 1/2... Batch 50... Discriminator Loss: 1.0549... Generator Loss: 2.6820
Epoch 1/2... Batch 60... Discriminator Loss: 0.9543... Generator Loss: 2.6311
Epoch 1/2... Batch 70... Discriminator Loss: 2.0254... Generator Loss: 0.9194
Epoch 1/2... Batch 80... Discriminator Loss: 1.8843... Generator Loss: 0.3194
Epoch 1/2... Batch 90... Discriminator Loss: 1.6510... Generator Loss: 0.3802
Epoch 1/2... Batch 100... Discriminator Loss: 1.3273... Generator Loss: 0.8794
Epoch 1/2... Batch 110... Discriminator Loss: 1.3622... Generator Loss: 0.9561
Epoch 1/2... Batch 120... Discriminator Loss: 1.5871... Generator Loss: 1.6485
Epoch 1/2... Batch 130... Discriminator Loss: 1.4429... Generator Loss: 1.1063
Epoch 1/2... Batch 140... Discriminator Loss: 1.3338... Generator Loss: 1.2156
Epoch 1/2... Batch 150... Discriminator Loss: 1.3495... Generator Loss: 1.2617
Epoch 1/2... Batch 160... Discriminator Loss: 1.3952... Generator Loss: 1.3500
Epoch 1/2... Batch 170... Discriminator Loss: 1.3192... Generator Loss: 1.1446
Epoch 1/2... Batch 180... Discriminator Loss: 1.3326... Generator Loss: 1.1287
Epoch 1/2... Batch 190... Discriminator Loss: 1.3194... Generator Loss: 0.9787
Epoch 1/2... Batch 200... Discriminator Loss: 1.4762... Generator Loss: 1.5617
Epoch 1/2... Batch 210... Discriminator Loss: 1.3189... Generator Loss: 1.1905
Epoch 1/2... Batch 220... Discriminator Loss: 1.2285... Generator Loss: 0.8059
Epoch 1/2... Batch 230... Discriminator Loss: 1.4483... Generator Loss: 0.5364
Epoch 1/2... Batch 240... Discriminator Loss: 1.3671... Generator Loss: 0.5807
Epoch 1/2... Batch 250... Discriminator Loss: 1.3348... Generator Loss: 0.8505
Epoch 1/2... Batch 260... Discriminator Loss: 1.4298... Generator Loss: 1.5502
Epoch 1/2... Batch 270... Discriminator Loss: 1.3799... Generator Loss: 1.3615
Epoch 1/2... Batch 280... Discriminator Loss: 1.4282... Generator Loss: 1.1518
Epoch 1/2... Batch 290... Discriminator Loss: 1.2856... Generator Loss: 1.0355
Epoch 1/2... Batch 300... Discriminator Loss: 1.6208... Generator Loss: 1.6760
Epoch 1/2... Batch 310... Discriminator Loss: 1.3371... Generator Loss: 1.0883
Epoch 1/2... Batch 320... Discriminator Loss: 1.3182... Generator Loss: 1.0579
Epoch 1/2... Batch 330... Discriminator Loss: 1.2722... Generator Loss: 1.4695
Epoch 1/2... Batch 340... Discriminator Loss: 1.3030... Generator Loss: 1.0452
Epoch 1/2... Batch 350... Discriminator Loss: 1.2987... Generator Loss: 1.2306
Epoch 1/2... Batch 360... Discriminator Loss: 1.4025... Generator Loss: 1.3825
Epoch 1/2... Batch 370... Discriminator Loss: 1.3281... Generator Loss: 0.9200
Epoch 1/2... Batch 380... Discriminator Loss: 1.2647... Generator Loss: 0.8065
Epoch 1/2... Batch 390... Discriminator Loss: 1.3499... Generator Loss: 1.2992
Epoch 1/2... Batch 400... Discriminator Loss: 1.3886... Generator Loss: 1.2909
Epoch 1/2... Batch 410... Discriminator Loss: 1.3076... Generator Loss: 0.7943
Epoch 1/2... Batch 420... Discriminator Loss: 1.4842... Generator Loss: 0.4487
Epoch 1/2... Batch 430... Discriminator Loss: 1.3480... Generator Loss: 0.5965
Epoch 1/2... Batch 440... Discriminator Loss: 1.3709... Generator Loss: 0.8251
Epoch 1/2... Batch 450... Discriminator Loss: 1.4216... Generator Loss: 1.4333
Epoch 1/2... Batch 460... Discriminator Loss: 1.3259... Generator Loss: 1.1502
Epoch 1/2... Batch 470... Discriminator Loss: 1.2786... Generator Loss: 1.2149
Epoch 1/2... Batch 480... Discriminator Loss: 1.2533... Generator Loss: 0.8795
Epoch 1/2... Batch 490... Discriminator Loss: 1.6634... Generator Loss: 1.8112
Epoch 1/2... Batch 500... Discriminator Loss: 1.3138... Generator Loss: 1.1673
Epoch 1/2... Batch 510... Discriminator Loss: 1.3030... Generator Loss: 1.3795
Epoch 1/2... Batch 520... Discriminator Loss: 1.2554... Generator Loss: 1.1127
Epoch 1/2... Batch 530... Discriminator Loss: 1.3541... Generator Loss: 0.5663
Epoch 1/2... Batch 540... Discriminator Loss: 1.5367... Generator Loss: 0.4857
Epoch 1/2... Batch 550... Discriminator Loss: 1.4362... Generator Loss: 0.5119
Epoch 1/2... Batch 560... Discriminator Loss: 1.4387... Generator Loss: 0.5208
Epoch 1/2... Batch 570... Discriminator Loss: 1.2411... Generator Loss: 0.7663
Epoch 1/2... Batch 580... Discriminator Loss: 1.3455... Generator Loss: 0.6681
Epoch 1/2... Batch 590... Discriminator Loss: 1.2980... Generator Loss: 1.0038
Epoch 1/2... Batch 600... Discriminator Loss: 1.4503... Generator Loss: 1.2248
Epoch 1/2... Batch 610... Discriminator Loss: 1.3808... Generator Loss: 1.4334
Epoch 1/2... Batch 620... Discriminator Loss: 1.3432... Generator Loss: 1.2155
Epoch 1/2... Batch 630... Discriminator Loss: 1.3299... Generator Loss: 1.3542
Epoch 1/2... Batch 640... Discriminator Loss: 1.2981... Generator Loss: 0.9397
Epoch 1/2... Batch 650... Discriminator Loss: 1.3131... Generator Loss: 1.1108
Epoch 1/2... Batch 660... Discriminator Loss: 1.2454... Generator Loss: 0.8368
Epoch 1/2... Batch 670... Discriminator Loss: 1.4566... Generator Loss: 0.4629
Epoch 1/2... Batch 680... Discriminator Loss: 1.3482... Generator Loss: 0.5805
Epoch 1/2... Batch 690... Discriminator Loss: 1.2486... Generator Loss: 0.7529
Epoch 1/2... Batch 700... Discriminator Loss: 1.4136... Generator Loss: 0.5572
Epoch 1/2... Batch 710... Discriminator Loss: 1.2888... Generator Loss: 0.6700
Epoch 1/2... Batch 720... Discriminator Loss: 1.3340... Generator Loss: 0.6015
Epoch 1/2... Batch 730... Discriminator Loss: 1.3458... Generator Loss: 0.5523
Epoch 1/2... Batch 740... Discriminator Loss: 1.4174... Generator Loss: 0.6140
Epoch 1/2... Batch 750... Discriminator Loss: 1.2470... Generator Loss: 0.8595
Epoch 1/2... Batch 760... Discriminator Loss: 1.3123... Generator Loss: 0.9118
Epoch 1/2... Batch 770... Discriminator Loss: 1.6358... Generator Loss: 0.3657
Epoch 1/2... Batch 780... Discriminator Loss: 1.2637... Generator Loss: 0.7147
Epoch 1/2... Batch 790... Discriminator Loss: 1.3308... Generator Loss: 0.5818
Epoch 1/2... Batch 800... Discriminator Loss: 1.2624... Generator Loss: 0.6948
Epoch 1/2... Batch 810... Discriminator Loss: 1.4550... Generator Loss: 0.4969
Epoch 1/2... Batch 820... Discriminator Loss: 1.2048... Generator Loss: 0.8732
Epoch 1/2... Batch 830... Discriminator Loss: 1.4182... Generator Loss: 0.6926
Epoch 1/2... Batch 840... Discriminator Loss: 1.3510... Generator Loss: 0.5901
Epoch 1/2... Batch 850... Discriminator Loss: 1.4588... Generator Loss: 0.4867
Epoch 1/2... Batch 860... Discriminator Loss: 1.2092... Generator Loss: 0.8581
Epoch 1/2... Batch 870... Discriminator Loss: 1.4215... Generator Loss: 0.4586
Epoch 1/2... Batch 880... Discriminator Loss: 1.2602... Generator Loss: 0.7414
Epoch 1/2... Batch 890... Discriminator Loss: 1.4889... Generator Loss: 0.4615
Epoch 1/2... Batch 900... Discriminator Loss: 1.2865... Generator Loss: 0.6466
Epoch 1/2... Batch 910... Discriminator Loss: 1.4170... Generator Loss: 0.5031
Epoch 1/2... Batch 920... Discriminator Loss: 1.2298... Generator Loss: 0.8485
Epoch 1/2... Batch 930... Discriminator Loss: 1.3698... Generator Loss: 1.3646
Epoch 2/2... Batch 940... Discriminator Loss: 1.2948... Generator Loss: 1.3553
Epoch 2/2... Batch 950... Discriminator Loss: 1.3220... Generator Loss: 0.9904
Epoch 2/2... Batch 960... Discriminator Loss: 1.3541... Generator Loss: 0.5714
Epoch 2/2... Batch 970... Discriminator Loss: 1.2599... Generator Loss: 0.9510
Epoch 2/2... Batch 980... Discriminator Loss: 1.2903... Generator Loss: 1.4652
Epoch 2/2... Batch 990... Discriminator Loss: 1.3258... Generator Loss: 1.1529
Epoch 2/2... Batch 1000... Discriminator Loss: 1.2797... Generator Loss: 1.1675
Epoch 2/2... Batch 1010... Discriminator Loss: 1.3616... Generator Loss: 0.5338
Epoch 2/2... Batch 1020... Discriminator Loss: 1.4967... Generator Loss: 0.5312
Epoch 2/2... Batch 1030... Discriminator Loss: 1.5782... Generator Loss: 0.9171
Epoch 2/2... Batch 1040... Discriminator Loss: 1.2936... Generator Loss: 0.6288
Epoch 2/2... Batch 1050... Discriminator Loss: 1.2673... Generator Loss: 0.7245
Epoch 2/2... Batch 1060... Discriminator Loss: 1.2863... Generator Loss: 0.6538
Epoch 2/2... Batch 1070... Discriminator Loss: 1.2687... Generator Loss: 1.0293
Epoch 2/2... Batch 1080... Discriminator Loss: 1.3849... Generator Loss: 1.5507
Epoch 2/2... Batch 1090... Discriminator Loss: 1.2529... Generator Loss: 1.0776
Epoch 2/2... Batch 1100... Discriminator Loss: 1.3462... Generator Loss: 0.7032
Epoch 2/2... Batch 1110... Discriminator Loss: 1.5315... Generator Loss: 0.9805
Epoch 2/2... Batch 1120... Discriminator Loss: 1.2573... Generator Loss: 0.7053
Epoch 2/2... Batch 1130... Discriminator Loss: 1.2439... Generator Loss: 0.9094
Epoch 2/2... Batch 1140... Discriminator Loss: 1.3507... Generator Loss: 1.4785
Epoch 2/2... Batch 1150... Discriminator Loss: 1.2910... Generator Loss: 1.2779
Epoch 2/2... Batch 1160... Discriminator Loss: 1.3276... Generator Loss: 1.4891
Epoch 2/2... Batch 1170... Discriminator Loss: 1.1980... Generator Loss: 1.0862
Epoch 2/2... Batch 1180... Discriminator Loss: 1.2667... Generator Loss: 1.3561
Epoch 2/2... Batch 1190... Discriminator Loss: 1.1990... Generator Loss: 0.8523
Epoch 2/2... Batch 1200... Discriminator Loss: 1.3957... Generator Loss: 1.2819
Epoch 2/2... Batch 1210... Discriminator Loss: 1.3102... Generator Loss: 1.2924
Epoch 2/2... Batch 1220... Discriminator Loss: 1.2292... Generator Loss: 1.2557
Epoch 2/2... Batch 1230... Discriminator Loss: 1.3066... Generator Loss: 1.3484
Epoch 2/2... Batch 1240... Discriminator Loss: 1.2701... Generator Loss: 1.2233
Epoch 2/2... Batch 1250... Discriminator Loss: 1.3550... Generator Loss: 1.6123
Epoch 2/2... Batch 1260... Discriminator Loss: 1.2283... Generator Loss: 1.3217
Epoch 2/2... Batch 1270... Discriminator Loss: 1.3327... Generator Loss: 1.6173
Epoch 2/2... Batch 1280... Discriminator Loss: 1.3005... Generator Loss: 1.4458
Epoch 2/2... Batch 1290... Discriminator Loss: 1.2201... Generator Loss: 0.7934
Epoch 2/2... Batch 1300... Discriminator Loss: 1.5486... Generator Loss: 0.4567
Epoch 2/2... Batch 1310... Discriminator Loss: 1.2737... Generator Loss: 0.8127
Epoch 2/2... Batch 1320... Discriminator Loss: 1.2631... Generator Loss: 0.6482
Epoch 2/2... Batch 1330... Discriminator Loss: 1.3536... Generator Loss: 0.6269
Epoch 2/2... Batch 1340... Discriminator Loss: 1.7396... Generator Loss: 0.8974
Epoch 2/2... Batch 1350... Discriminator Loss: 1.1945... Generator Loss: 1.0280
Epoch 2/2... Batch 1360... Discriminator Loss: 1.2545... Generator Loss: 1.0780
Epoch 2/2... Batch 1370... Discriminator Loss: 1.2817... Generator Loss: 1.2400
Epoch 2/2... Batch 1380... Discriminator Loss: 1.3460... Generator Loss: 1.4722
Epoch 2/2... Batch 1390... Discriminator Loss: 1.1896... Generator Loss: 1.1689
Epoch 2/2... Batch 1400... Discriminator Loss: 1.2466... Generator Loss: 1.1875
Epoch 2/2... Batch 1410... Discriminator Loss: 1.2055... Generator Loss: 0.9617
Epoch 2/2... Batch 1420... Discriminator Loss: 1.1821... Generator Loss: 0.9173
Epoch 2/2... Batch 1430... Discriminator Loss: 1.5510... Generator Loss: 0.4120
Epoch 2/2... Batch 1440... Discriminator Loss: 1.1881... Generator Loss: 0.8509
Epoch 2/2... Batch 1450... Discriminator Loss: 1.4522... Generator Loss: 1.4744
Epoch 2/2... Batch 1460... Discriminator Loss: 1.1526... Generator Loss: 0.9376
Epoch 2/2... Batch 1470... Discriminator Loss: 1.2714... Generator Loss: 0.6922
Epoch 2/2... Batch 1480... Discriminator Loss: 1.2121... Generator Loss: 0.7328
Epoch 2/2... Batch 1490... Discriminator Loss: 1.6547... Generator Loss: 0.3674
Epoch 2/2... Batch 1500... Discriminator Loss: 1.2540... Generator Loss: 0.7140
Epoch 2/2... Batch 1510... Discriminator Loss: 1.2485... Generator Loss: 0.6797
Epoch 2/2... Batch 1520... Discriminator Loss: 1.1595... Generator Loss: 0.9844
Epoch 2/2... Batch 1530... Discriminator Loss: 1.3312... Generator Loss: 0.5983
Epoch 2/2... Batch 1540... Discriminator Loss: 1.4224... Generator Loss: 0.6418
Epoch 2/2... Batch 1550... Discriminator Loss: 1.2047... Generator Loss: 0.9809
Epoch 2/2... Batch 1560... Discriminator Loss: 1.2599... Generator Loss: 0.8203
Epoch 2/2... Batch 1570... Discriminator Loss: 1.2178... Generator Loss: 0.7066
Epoch 2/2... Batch 1580... Discriminator Loss: 1.1883... Generator Loss: 1.0262
Epoch 2/2... Batch 1590... Discriminator Loss: 1.3209... Generator Loss: 1.3643
Epoch 2/2... Batch 1600... Discriminator Loss: 1.3859... Generator Loss: 1.4897
Epoch 2/2... Batch 1610... Discriminator Loss: 1.2313... Generator Loss: 1.1930
Epoch 2/2... Batch 1620... Discriminator Loss: 1.1828... Generator Loss: 0.8846
Epoch 2/2... Batch 1630... Discriminator Loss: 1.3664... Generator Loss: 0.5629
Epoch 2/2... Batch 1640... Discriminator Loss: 1.2803... Generator Loss: 0.6065
Epoch 2/2... Batch 1650... Discriminator Loss: 1.2317... Generator Loss: 0.8532
Epoch 2/2... Batch 1660... Discriminator Loss: 1.0822... Generator Loss: 1.1603
Epoch 2/2... Batch 1670... Discriminator Loss: 1.5338... Generator Loss: 1.8083
Epoch 2/2... Batch 1680... Discriminator Loss: 1.2256... Generator Loss: 1.0410
Epoch 2/2... Batch 1690... Discriminator Loss: 1.1529... Generator Loss: 0.8289
Epoch 2/2... Batch 1700... Discriminator Loss: 1.2752... Generator Loss: 0.6250
Epoch 2/2... Batch 1710... Discriminator Loss: 1.2098... Generator Loss: 1.0365
Epoch 2/2... Batch 1720... Discriminator Loss: 1.1638... Generator Loss: 0.8693
Epoch 2/2... Batch 1730... Discriminator Loss: 1.8723... Generator Loss: 1.0022
Epoch 2/2... Batch 1740... Discriminator Loss: 1.1868... Generator Loss: 0.7715
Epoch 2/2... Batch 1750... Discriminator Loss: 1.2114... Generator Loss: 0.6803
Epoch 2/2... Batch 1760... Discriminator Loss: 1.3893... Generator Loss: 0.5203
Epoch 2/2... Batch 1770... Discriminator Loss: 1.3748... Generator Loss: 0.5499
Epoch 2/2... Batch 1780... Discriminator Loss: 1.1481... Generator Loss: 0.8150
Epoch 2/2... Batch 1790... Discriminator Loss: 1.3663... Generator Loss: 0.5919
Epoch 2/2... Batch 1800... Discriminator Loss: 1.2569... Generator Loss: 0.7610
Epoch 2/2... Batch 1810... Discriminator Loss: 1.2804... Generator Loss: 1.1458
Epoch 2/2... Batch 1820... Discriminator Loss: 1.3720... Generator Loss: 0.5114
Epoch 2/2... Batch 1830... Discriminator Loss: 1.2124... Generator Loss: 0.8566
Epoch 2/2... Batch 1840... Discriminator Loss: 1.3316... Generator Loss: 0.5787
Epoch 2/2... Batch 1850... Discriminator Loss: 1.3088... Generator Loss: 0.6390
Epoch 2/2... Batch 1860... Discriminator Loss: 1.1811... Generator Loss: 0.7571
Epoch 2/2... Batch 1870... Discriminator Loss: 1.1343... Generator Loss: 0.8904

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 128
learning_rate = 2e-4
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.3855... Generator Loss: 0.9123
Epoch 1/1... Batch 20... Discriminator Loss: 1.4303... Generator Loss: 0.9143
Epoch 1/1... Batch 30... Discriminator Loss: 1.3075... Generator Loss: 0.9300
Epoch 1/1... Batch 40... Discriminator Loss: 1.2435... Generator Loss: 0.9468
Epoch 1/1... Batch 50... Discriminator Loss: 1.2347... Generator Loss: 1.0979
Epoch 1/1... Batch 60... Discriminator Loss: 1.2527... Generator Loss: 1.0317
Epoch 1/1... Batch 70... Discriminator Loss: 1.1649... Generator Loss: 1.0745
Epoch 1/1... Batch 80... Discriminator Loss: 1.1576... Generator Loss: 0.9494
Epoch 1/1... Batch 90... Discriminator Loss: 1.1203... Generator Loss: 1.1657
Epoch 1/1... Batch 100... Discriminator Loss: 0.9440... Generator Loss: 1.3398
Epoch 1/1... Batch 110... Discriminator Loss: 0.9371... Generator Loss: 1.4091
Epoch 1/1... Batch 120... Discriminator Loss: 0.8584... Generator Loss: 1.3821
Epoch 1/1... Batch 130... Discriminator Loss: 0.9140... Generator Loss: 1.3825
Epoch 1/1... Batch 140... Discriminator Loss: 0.9423... Generator Loss: 1.4118
Epoch 1/1... Batch 150... Discriminator Loss: 0.8319... Generator Loss: 1.5416
Epoch 1/1... Batch 160... Discriminator Loss: 0.7667... Generator Loss: 1.4920
Epoch 1/1... Batch 170... Discriminator Loss: 0.8378... Generator Loss: 1.4831
Epoch 1/1... Batch 180... Discriminator Loss: 0.8385... Generator Loss: 1.3358
Epoch 1/1... Batch 190... Discriminator Loss: 0.9652... Generator Loss: 1.1979
Epoch 1/1... Batch 200... Discriminator Loss: 0.9481... Generator Loss: 1.5867
Epoch 1/1... Batch 210... Discriminator Loss: 0.9195... Generator Loss: 1.7051
Epoch 1/1... Batch 220... Discriminator Loss: 0.6390... Generator Loss: 1.6960
Epoch 1/1... Batch 230... Discriminator Loss: 0.6693... Generator Loss: 1.8012
Epoch 1/1... Batch 240... Discriminator Loss: 0.7725... Generator Loss: 1.6335
Epoch 1/1... Batch 250... Discriminator Loss: 0.5713... Generator Loss: 2.2579
Epoch 1/1... Batch 260... Discriminator Loss: 0.5300... Generator Loss: 2.1917
Epoch 1/1... Batch 270... Discriminator Loss: 1.3416... Generator Loss: 2.1027
Epoch 1/1... Batch 280... Discriminator Loss: 0.5348... Generator Loss: 2.2506
Epoch 1/1... Batch 290... Discriminator Loss: 0.5341... Generator Loss: 2.2995
Epoch 1/1... Batch 300... Discriminator Loss: 0.4536... Generator Loss: 2.5807
Epoch 1/1... Batch 310... Discriminator Loss: 0.4853... Generator Loss: 2.4007
Epoch 1/1... Batch 320... Discriminator Loss: 0.5457... Generator Loss: 2.0603
Epoch 1/1... Batch 330... Discriminator Loss: 0.6219... Generator Loss: 1.8124
Epoch 1/1... Batch 340... Discriminator Loss: 0.5746... Generator Loss: 1.9727
Epoch 1/1... Batch 350... Discriminator Loss: 0.6107... Generator Loss: 2.2133
Epoch 1/1... Batch 360... Discriminator Loss: 0.8823... Generator Loss: 1.1704
Epoch 1/1... Batch 370... Discriminator Loss: 0.7299... Generator Loss: 2.0396
Epoch 1/1... Batch 380... Discriminator Loss: 0.5752... Generator Loss: 1.7552
Epoch 1/1... Batch 390... Discriminator Loss: 0.6862... Generator Loss: 1.9617
Epoch 1/1... Batch 400... Discriminator Loss: 0.8412... Generator Loss: 1.4033
Epoch 1/1... Batch 410... Discriminator Loss: 0.7147... Generator Loss: 1.6903
Epoch 1/1... Batch 420... Discriminator Loss: 0.6336... Generator Loss: 1.7691
Epoch 1/1... Batch 430... Discriminator Loss: 0.6042... Generator Loss: 1.7449
Epoch 1/1... Batch 440... Discriminator Loss: 0.6350... Generator Loss: 1.6958
Epoch 1/1... Batch 450... Discriminator Loss: 0.7769... Generator Loss: 1.4031
Epoch 1/1... Batch 460... Discriminator Loss: 0.5563... Generator Loss: 1.8806
Epoch 1/1... Batch 470... Discriminator Loss: 0.7001... Generator Loss: 1.8339
Epoch 1/1... Batch 480... Discriminator Loss: 0.5724... Generator Loss: 1.8447
Epoch 1/1... Batch 490... Discriminator Loss: 0.8566... Generator Loss: 1.4576
Epoch 1/1... Batch 500... Discriminator Loss: 0.5392... Generator Loss: 1.9551
Epoch 1/1... Batch 510... Discriminator Loss: 0.5263... Generator Loss: 2.1166
Epoch 1/1... Batch 520... Discriminator Loss: 0.6020... Generator Loss: 2.0046
Epoch 1/1... Batch 530... Discriminator Loss: 0.5419... Generator Loss: 2.0019
Epoch 1/1... Batch 540... Discriminator Loss: 0.7903... Generator Loss: 1.8799
Epoch 1/1... Batch 550... Discriminator Loss: 0.4989... Generator Loss: 2.1771
Epoch 1/1... Batch 560... Discriminator Loss: 0.5677... Generator Loss: 1.8065
Epoch 1/1... Batch 570... Discriminator Loss: 0.5446... Generator Loss: 1.9116
Epoch 1/1... Batch 580... Discriminator Loss: 1.1925... Generator Loss: 1.3243
Epoch 1/1... Batch 590... Discriminator Loss: 1.4117... Generator Loss: 1.8078
Epoch 1/1... Batch 600... Discriminator Loss: 0.7224... Generator Loss: 1.6415
Epoch 1/1... Batch 610... Discriminator Loss: 0.7031... Generator Loss: 1.5019
Epoch 1/1... Batch 620... Discriminator Loss: 0.7166... Generator Loss: 1.4179
Epoch 1/1... Batch 630... Discriminator Loss: 0.9346... Generator Loss: 1.0080
Epoch 1/1... Batch 640... Discriminator Loss: 0.7278... Generator Loss: 1.3247
Epoch 1/1... Batch 650... Discriminator Loss: 0.5414... Generator Loss: 2.5356
Epoch 1/1... Batch 660... Discriminator Loss: 0.5774... Generator Loss: 1.7398
Epoch 1/1... Batch 670... Discriminator Loss: 0.7519... Generator Loss: 1.4905
Epoch 1/1... Batch 680... Discriminator Loss: 0.6004... Generator Loss: 1.9386
Epoch 1/1... Batch 690... Discriminator Loss: 1.0265... Generator Loss: 1.0106
Epoch 1/1... Batch 700... Discriminator Loss: 0.9552... Generator Loss: 1.0222
Epoch 1/1... Batch 710... Discriminator Loss: 1.2891... Generator Loss: 1.4121
Epoch 1/1... Batch 720... Discriminator Loss: 1.9105... Generator Loss: 0.7526
Epoch 1/1... Batch 730... Discriminator Loss: 1.1302... Generator Loss: 0.9287
Epoch 1/1... Batch 740... Discriminator Loss: 0.9303... Generator Loss: 1.2039
Epoch 1/1... Batch 750... Discriminator Loss: 0.8980... Generator Loss: 1.3748
Epoch 1/1... Batch 760... Discriminator Loss: 0.9443... Generator Loss: 1.2569
Epoch 1/1... Batch 770... Discriminator Loss: 1.0028... Generator Loss: 1.1278
Epoch 1/1... Batch 780... Discriminator Loss: 1.3473... Generator Loss: 0.5702
Epoch 1/1... Batch 790... Discriminator Loss: 0.9689... Generator Loss: 1.4496
Epoch 1/1... Batch 800... Discriminator Loss: 1.1746... Generator Loss: 1.2657
Epoch 1/1... Batch 810... Discriminator Loss: 0.9598... Generator Loss: 1.4913
Epoch 1/1... Batch 820... Discriminator Loss: 0.9915... Generator Loss: 1.1962
Epoch 1/1... Batch 830... Discriminator Loss: 0.9238... Generator Loss: 1.1016
Epoch 1/1... Batch 840... Discriminator Loss: 1.3214... Generator Loss: 0.7470
Epoch 1/1... Batch 850... Discriminator Loss: 0.8313... Generator Loss: 1.2248
Epoch 1/1... Batch 860... Discriminator Loss: 1.1930... Generator Loss: 1.0425
Epoch 1/1... Batch 870... Discriminator Loss: 1.1814... Generator Loss: 1.1052
Epoch 1/1... Batch 880... Discriminator Loss: 1.0171... Generator Loss: 1.2386
Epoch 1/1... Batch 890... Discriminator Loss: 0.9326... Generator Loss: 1.2015
Epoch 1/1... Batch 900... Discriminator Loss: 0.9221... Generator Loss: 1.4798
Epoch 1/1... Batch 910... Discriminator Loss: 1.0974... Generator Loss: 0.9761
Epoch 1/1... Batch 920... Discriminator Loss: 0.9789... Generator Loss: 1.2896
Epoch 1/1... Batch 930... Discriminator Loss: 0.9369... Generator Loss: 1.3887
Epoch 1/1... Batch 940... Discriminator Loss: 0.9055... Generator Loss: 1.4154
Epoch 1/1... Batch 950... Discriminator Loss: 0.9872... Generator Loss: 1.0274
Epoch 1/1... Batch 960... Discriminator Loss: 1.0417... Generator Loss: 1.1430
Epoch 1/1... Batch 970... Discriminator Loss: 0.8303... Generator Loss: 1.5598
Epoch 1/1... Batch 980... Discriminator Loss: 1.2920... Generator Loss: 1.2354
Epoch 1/1... Batch 990... Discriminator Loss: 1.1636... Generator Loss: 0.9091
Epoch 1/1... Batch 1000... Discriminator Loss: 1.0097... Generator Loss: 0.9758
Epoch 1/1... Batch 1010... Discriminator Loss: 1.0560... Generator Loss: 1.2822
Epoch 1/1... Batch 1020... Discriminator Loss: 1.2349... Generator Loss: 1.2608
Epoch 1/1... Batch 1030... Discriminator Loss: 1.0539... Generator Loss: 0.9228
Epoch 1/1... Batch 1040... Discriminator Loss: 1.2362... Generator Loss: 0.7448
Epoch 1/1... Batch 1050... Discriminator Loss: 0.9436... Generator Loss: 1.1679
Epoch 1/1... Batch 1060... Discriminator Loss: 1.0071... Generator Loss: 1.4543
Epoch 1/1... Batch 1070... Discriminator Loss: 1.1965... Generator Loss: 0.9761
Epoch 1/1... Batch 1080... Discriminator Loss: 1.0018... Generator Loss: 0.9523
Epoch 1/1... Batch 1090... Discriminator Loss: 0.9506... Generator Loss: 1.3044
Epoch 1/1... Batch 1100... Discriminator Loss: 0.9524... Generator Loss: 1.1293
Epoch 1/1... Batch 1110... Discriminator Loss: 1.0325... Generator Loss: 1.0487
Epoch 1/1... Batch 1120... Discriminator Loss: 1.0724... Generator Loss: 1.3514
Epoch 1/1... Batch 1130... Discriminator Loss: 1.0890... Generator Loss: 1.1044
Epoch 1/1... Batch 1140... Discriminator Loss: 1.1776... Generator Loss: 1.3924
Epoch 1/1... Batch 1150... Discriminator Loss: 1.1204... Generator Loss: 1.3141
Epoch 1/1... Batch 1160... Discriminator Loss: 1.1745... Generator Loss: 0.9522
Epoch 1/1... Batch 1170... Discriminator Loss: 1.0163... Generator Loss: 1.1256
Epoch 1/1... Batch 1180... Discriminator Loss: 1.4131... Generator Loss: 0.8686
Epoch 1/1... Batch 1190... Discriminator Loss: 1.0134... Generator Loss: 1.1518
Epoch 1/1... Batch 1200... Discriminator Loss: 1.1828... Generator Loss: 1.0805
Epoch 1/1... Batch 1210... Discriminator Loss: 1.1724... Generator Loss: 1.0169
Epoch 1/1... Batch 1220... Discriminator Loss: 1.0171... Generator Loss: 1.1733
Epoch 1/1... Batch 1230... Discriminator Loss: 1.3077... Generator Loss: 1.1165
Epoch 1/1... Batch 1240... Discriminator Loss: 1.2550... Generator Loss: 0.9473
Epoch 1/1... Batch 1250... Discriminator Loss: 1.0511... Generator Loss: 1.0751
Epoch 1/1... Batch 1260... Discriminator Loss: 1.2747... Generator Loss: 0.7017
Epoch 1/1... Batch 1270... Discriminator Loss: 1.2096... Generator Loss: 1.1808
Epoch 1/1... Batch 1280... Discriminator Loss: 1.2561... Generator Loss: 0.6199
Epoch 1/1... Batch 1290... Discriminator Loss: 1.1647... Generator Loss: 1.0820
Epoch 1/1... Batch 1300... Discriminator Loss: 0.9878... Generator Loss: 1.0908
Epoch 1/1... Batch 1310... Discriminator Loss: 1.2054... Generator Loss: 0.9014
Epoch 1/1... Batch 1320... Discriminator Loss: 1.1157... Generator Loss: 0.9162
Epoch 1/1... Batch 1330... Discriminator Loss: 0.9777... Generator Loss: 1.1093
Epoch 1/1... Batch 1340... Discriminator Loss: 1.1438... Generator Loss: 0.7844
Epoch 1/1... Batch 1350... Discriminator Loss: 1.1747... Generator Loss: 1.3835
Epoch 1/1... Batch 1360... Discriminator Loss: 1.1246... Generator Loss: 1.0355
Epoch 1/1... Batch 1370... Discriminator Loss: 1.1223... Generator Loss: 1.0824
Epoch 1/1... Batch 1380... Discriminator Loss: 1.6011... Generator Loss: 0.4177
Epoch 1/1... Batch 1390... Discriminator Loss: 1.0961... Generator Loss: 0.9596
Epoch 1/1... Batch 1400... Discriminator Loss: 1.0884... Generator Loss: 0.9522
Epoch 1/1... Batch 1410... Discriminator Loss: 1.4065... Generator Loss: 0.8757
Epoch 1/1... Batch 1420... Discriminator Loss: 1.1301... Generator Loss: 1.1145
Epoch 1/1... Batch 1430... Discriminator Loss: 1.1205... Generator Loss: 0.9879
Epoch 1/1... Batch 1440... Discriminator Loss: 1.1974... Generator Loss: 1.0657
Epoch 1/1... Batch 1450... Discriminator Loss: 1.3222... Generator Loss: 0.8501
Epoch 1/1... Batch 1460... Discriminator Loss: 1.1821... Generator Loss: 1.0148
Epoch 1/1... Batch 1470... Discriminator Loss: 1.1473... Generator Loss: 1.1008
Epoch 1/1... Batch 1480... Discriminator Loss: 1.0707... Generator Loss: 1.1810
Epoch 1/1... Batch 1490... Discriminator Loss: 1.0636... Generator Loss: 1.2080
Epoch 1/1... Batch 1500... Discriminator Loss: 1.2119... Generator Loss: 1.0028
Epoch 1/1... Batch 1510... Discriminator Loss: 1.1455... Generator Loss: 0.8511
Epoch 1/1... Batch 1520... Discriminator Loss: 1.1047... Generator Loss: 1.1325
Epoch 1/1... Batch 1530... Discriminator Loss: 1.1170... Generator Loss: 0.9186
Epoch 1/1... Batch 1540... Discriminator Loss: 1.2317... Generator Loss: 0.7421
Epoch 1/1... Batch 1550... Discriminator Loss: 1.2752... Generator Loss: 0.8214
Epoch 1/1... Batch 1560... Discriminator Loss: 1.1372... Generator Loss: 1.0433
Epoch 1/1... Batch 1570... Discriminator Loss: 1.1911... Generator Loss: 1.0372
Epoch 1/1... Batch 1580... Discriminator Loss: 1.1885... Generator Loss: 1.0946
Epoch 1/1... Batch 1590... Discriminator Loss: 1.1384... Generator Loss: 1.0077
Epoch 1/1... Batch 1600... Discriminator Loss: 1.2343... Generator Loss: 0.9182
Epoch 1/1... Batch 1610... Discriminator Loss: 1.1814... Generator Loss: 0.9188
Epoch 1/1... Batch 1620... Discriminator Loss: 1.0911... Generator Loss: 1.2134
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Epoch 1/1... Batch 4880... Discriminator Loss: 1.1546... Generator Loss: 0.9826
Epoch 1/1... Batch 4890... Discriminator Loss: 1.2707... Generator Loss: 0.8858
Epoch 1/1... Batch 4900... Discriminator Loss: 1.2373... Generator Loss: 0.8582
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Epoch 1/1... Batch 4930... Discriminator Loss: 1.3490... Generator Loss: 0.7964
Epoch 1/1... Batch 4940... Discriminator Loss: 1.1938... Generator Loss: 0.8749
Epoch 1/1... Batch 4950... Discriminator Loss: 1.2325... Generator Loss: 0.9943
Epoch 1/1... Batch 4960... Discriminator Loss: 1.4541... Generator Loss: 1.0689
Epoch 1/1... Batch 4970... Discriminator Loss: 1.1800... Generator Loss: 0.9460
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Epoch 1/1... Batch 5010... Discriminator Loss: 1.2905... Generator Loss: 0.8339
Epoch 1/1... Batch 5020... Discriminator Loss: 1.2587... Generator Loss: 0.9149
Epoch 1/1... Batch 5030... Discriminator Loss: 1.1833... Generator Loss: 1.0125
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Epoch 1/1... Batch 5210... Discriminator Loss: 1.2216... Generator Loss: 0.8907
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Epoch 1/1... Batch 5370... Discriminator Loss: 1.3641... Generator Loss: 0.8662
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Epoch 1/1... Batch 5390... Discriminator Loss: 1.1976... Generator Loss: 0.8943
Epoch 1/1... Batch 5400... Discriminator Loss: 1.3781... Generator Loss: 0.7611
Epoch 1/1... Batch 5410... Discriminator Loss: 1.3472... Generator Loss: 0.8179
Epoch 1/1... Batch 5420... Discriminator Loss: 1.1393... Generator Loss: 0.9301
Epoch 1/1... Batch 5430... Discriminator Loss: 1.2289... Generator Loss: 1.0012
Epoch 1/1... Batch 5440... Discriminator Loss: 1.0657... Generator Loss: 1.1099
Epoch 1/1... Batch 5450... Discriminator Loss: 1.2822... Generator Loss: 0.8168
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Epoch 1/1... Batch 5480... Discriminator Loss: 1.2160... Generator Loss: 0.9223
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Epoch 1/1... Batch 5530... Discriminator Loss: 1.1322... Generator Loss: 0.9231
Epoch 1/1... Batch 5540... Discriminator Loss: 1.2049... Generator Loss: 0.8207
Epoch 1/1... Batch 5550... Discriminator Loss: 1.1987... Generator Loss: 0.8220
Epoch 1/1... Batch 5560... Discriminator Loss: 1.1159... Generator Loss: 0.9827
Epoch 1/1... Batch 5570... Discriminator Loss: 1.3469... Generator Loss: 0.7593
Epoch 1/1... Batch 5580... Discriminator Loss: 1.1915... Generator Loss: 1.0228
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Epoch 1/1... Batch 5600... Discriminator Loss: 1.2054... Generator Loss: 0.9900
Epoch 1/1... Batch 5610... Discriminator Loss: 1.1777... Generator Loss: 0.9348
Epoch 1/1... Batch 5620... Discriminator Loss: 1.3052... Generator Loss: 1.2682
Epoch 1/1... Batch 5630... Discriminator Loss: 1.1507... Generator Loss: 1.0352
Epoch 1/1... Batch 5640... Discriminator Loss: 1.3631... Generator Loss: 0.8455
Epoch 1/1... Batch 5650... Discriminator Loss: 1.0675... Generator Loss: 0.9510
Epoch 1/1... Batch 5660... Discriminator Loss: 1.0908... Generator Loss: 0.9269
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Epoch 1/1... Batch 5700... Discriminator Loss: 1.1639... Generator Loss: 0.7245
Epoch 1/1... Batch 5710... Discriminator Loss: 1.2220... Generator Loss: 0.8422
Epoch 1/1... Batch 5720... Discriminator Loss: 1.2310... Generator Loss: 0.8202
Epoch 1/1... Batch 5730... Discriminator Loss: 1.2181... Generator Loss: 0.9303
Epoch 1/1... Batch 5740... Discriminator Loss: 1.2757... Generator Loss: 0.8265
Epoch 1/1... Batch 5750... Discriminator Loss: 1.5863... Generator Loss: 1.0528
Epoch 1/1... Batch 5760... Discriminator Loss: 1.2700... Generator Loss: 0.8399
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Epoch 1/1... Batch 5780... Discriminator Loss: 1.2194... Generator Loss: 0.9202
Epoch 1/1... Batch 5790... Discriminator Loss: 1.2918... Generator Loss: 0.8962
Epoch 1/1... Batch 5800... Discriminator Loss: 1.1984... Generator Loss: 0.8828
Epoch 1/1... Batch 5810... Discriminator Loss: 1.1774... Generator Loss: 0.9422
Epoch 1/1... Batch 5820... Discriminator Loss: 1.4986... Generator Loss: 0.6210
Epoch 1/1... Batch 5830... Discriminator Loss: 1.3067... Generator Loss: 0.8631
Epoch 1/1... Batch 5840... Discriminator Loss: 1.3078... Generator Loss: 0.8547
Epoch 1/1... Batch 5850... Discriminator Loss: 1.1475... Generator Loss: 0.8040

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.